- What makes a PRD generator good for AI coding agents like Cursor or Claude Code?
- AI coding agents execute from the prompt they receive. A vague feature description produces inconsistent output. A structured spec with acceptance criteria per subtask, file references, dependency ordering, and explicit scope boundaries produces code that fits the existing system. The best PRD generators for AI coding agents read the codebase before generating — so the spec reflects what's actually in the repo, not what sounds abstractly correct.
- How is Tekk different from ChatPRD?
- ChatPRD is a chat-based platform optimized for PM workflows — stakeholder-facing documents, strategic gap analysis, CPO-level review. It doesn't read your codebase. Tekk reads your repository before asking any questions, grounds the spec in your actual database schema, API patterns, and architecture, and produces output that includes file references and acceptance criteria at the code level. Different primary uses: ChatPRD for PM deliverables, Tekk for developer-executable specifications.
- Is Tekk a PRD template tool?
- No. Tekk doesn't fill a template from your description. It reads your codebase, asks informed questions based on what it found, and generates a specification that reflects your actual system. The output structure is consistent (TL;DR, scope, subtasks, assumptions, validation) but the content is grounded in your specific repo, not a generic template.
- How does Tekk handle scope protection in the PRD?
- Every plan Tekk generates includes an explicit "Not Building" section as a required component. Before any code is written, you know exactly what's in scope and what's out. This is a deliberate design choice: scope creep is the default in most AI planning workflows. Tekk makes the boundaries explicit as part of the spec, not as an afterthought.
- Can I use Tekk's PRD generator without a codebase?
- Tekk works best with a connected repository — that's where the codebase-first advantage comes from. For greenfield projects with no code yet, Tekk can still generate structured specs from your description and web research, but the output doesn't carry the same architectural precision as codebase-grounded generation.
- What format does the Tekk PRD output use?
- Plans stream in real-time into a BlockNote rich text editor — editable immediately, stored persistently, connected to your Kanban board. The spec includes: TL;DR, Building/Not Building scope boundaries, subtasks with acceptance criteria and file references, assumptions with risk levels, and validation scenarios. It's a living document, not a PDF or a chat export.
- How does Tekk's PRD generator handle features with architectural tradeoffs?
- For features with real implementation choices, Tekk presents 2-3 architecturally distinct approaches before generating the spec. Each option includes honest tradeoffs — what works, what breaks, what you lose. You choose the direction. This deliberation step is embedded in the planning workflow, not something you do separately before using the tool.
- Who should use a codebase-aware PRD generator vs. a generic one?
- If your PRDs are primarily stakeholder communication artifacts — investor updates, board presentations, product strategy documents — a generic tool is fine. If your PRDs are the input to AI coding agent execution, you need codebase awareness. The spec needs to reflect what's actually in the repo: existing patterns, file locations, database schema, dependency structure. Without that grounding, the spec produces rework. With it, the first execution attempt is typically correct.
